Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan 650500, P. R. China.
Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, Yunnan 650500, P. R. China.
Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab432.
Cancer is thought to be caused by the accumulation of driver genetic mutations. Therefore, identifying cancer driver genes plays a crucial role in understanding the molecular mechanism of cancer and developing precision therapies and biomarkers. In this work, we propose a Multi-Task learning method, called MTGCN, based on the Graph Convolutional Network to identify cancer driver genes. First, we augment gene features by introducing their features on the protein-protein interaction (PPI) network. After that, the multi-task learning framework propagates and aggregates nodes and graph features from input to next layer to learn node embedding features, simultaneously optimizing the node prediction task and the link prediction task. Finally, we use a Bayesian task weight learner to balance the two tasks automatically. The outputs of MTGCN assign each gene a probability of being a cancer driver gene. Our method and the other four existing methods are applied to predict cancer drivers for pan-cancer and some single cancer types. The experimental results show that our model shows outstanding performance compared with the state-of-the-art methods in terms of the area under the Receiver Operating Characteristic (ROC) curves and the area under the precision-recall curves. The MTGCN is freely available via https://github.com/weiba/MTGCN.
癌症被认为是由驱动基因突变的积累引起的。因此，鉴定癌症驱动基因对于理解癌症的分子机制以及开发精准治疗和生物标志物至关重要。在这项工作中，我们提出了一种基于图卷积网络的多任务学习方法，称为 MTGCN，用于识别癌症驱动基因。首先，我们通过引入基因在蛋白质-蛋白质相互作用（PPI）网络上的特征来扩充基因特征。之后，多任务学习框架从输入到下一层传播和聚合节点和图特征，以学习节点嵌入特征，同时优化节点预测任务和链接预测任务。最后，我们使用贝叶斯任务权重学习器自动平衡这两个任务。MTGCN 的输出为每个基因分配成为癌症驱动基因的概率。我们的方法和其他四种现有方法被应用于预测泛癌和一些单癌型的癌症驱动基因。实验结果表明，与最先进的方法相比，我们的模型在接收器操作特征（ROC）曲线下面积和精度-召回曲线下面积方面表现出色。MTGCN 可通过 https://github.com/weiba/MTGCN 免费获得。
